This is my code, I want to read from this captcha image.
screenshot_name = "captcha.png"
driver.save_screenshot(screenshot_name)
#crop img
img = Image.open("captcha.png")
area = (30, 138, 355, 210)
cropped_img = img.crop(area)
cropped_img.save('captcha1.png')
#read Captcha
img = cv2.imread("captcha1.png")
gry = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
(h, w) = gry.shape[:2]
gry = cv2.resize(gry, (w*2, h*2))
cv2.imwrite("captcha2.png",gry)
cls = cv2.morphologyEx(gry, cv2.MORPH_CLOSE, None)
thr = cv2.threshold(cls, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imwrite("captcha3.png",thr)
txt = tess.image_to_string(thr)
print(txt)
I try this code and txt print me nothing, pls help.
Related
I am working on a opencv project, where I need to detect names column and any black color border present around the ROI. I am quite new with image processing so unable to figure out how to do this.
This is one of the sample images from which I wish to remove the column on the right (one containing all the details). But not all images contain this column, so I wish to detect the column and remove it from the image.
Here is the expected output.
EDIT
Here is the code that I have tried (I have tried using detection of largest rectangles in the region):
import cv2
from cv2 import dilate
from cv2 import findContours
import imutils
import numpy as np
image_name = 'test2.jpg'
og_plan = cv2.imread('test_images/{}'.format(image_name))
res = og_plan.copy()
img_height, img_width, img_channel = og_plan.shape
img_area = img_width * img_height
if og_plan.shape[0] > 800:
res = imutils.resize(res, height=720)
img_height, img_width, img_channel = res.shape
img_area = img_width * img_height
print(res.shape)
print(img_area)
hsv_plan = cv2.cvtColor(res, cv2.COLOR_BGR2HSV)
grey_plan = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
blue_min = np.array([14,100,76])
blue_max = np.array([130,255,255])
bluemask = cv2.inRange(hsv_plan,blue_min,blue_max)
blue_output = cv2.bitwise_and(hsv_plan, hsv_plan, mask=bluemask)
grey_mask = cv2.cvtColor(blue_output, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(grey_mask, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
ret2, thresh2 = cv2.threshold(grey_plan, 160, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
kernel = np.ones((3,3), np.uint8)
dil = dilate(thresh, kernel, iterations=2)
dil_grey = dilate(thresh2, kernel, iterations=2)
cont,hier = findContours(dil, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont1,hier1 = findContours(dil_grey, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
def max_rect(cntrs):
ar = {}
for cnt in cntrs:
x,y,w,h = cv2.boundingRect(cnt)
area = w*h
ar[area] = (x,y,w,h)
# ar = sorted(ar, key=ar.keys, reverse=True)
return ar
area_dict = max_rect(cont1)
roi_area = []
for area in area_dict:
if area >= img_area*0.1 and area < img_area:
print(area)
roi_area.append(area)
plan_no = 1
for a in roi_area:
plan = area_dict[a]
# del area_dict[a]
x,y,w,h = plan
roi = res[y:y+h, x:x+w]
print(plan)
cv2.rectangle(res, (x-5,y-5), (x+w+5, y+h+5), (255,255,0), 2)
cv2.imshow('ROI-{}'.format(image_name),roi)
cv2.imwrite('./result/{}_plan-{}.png'.format(image_name,plan_no),roi)
cv2.waitKey(0)
plan_no += 1
'''plan1 = area_dict[max(area_dict)]
del area_dict[max(area_dict)]
plan2 = area_dict[max(area_dict)]
x,y,w,h = plan1
x1,y1,w1,h1 = plan2
roi1 = res[y:y+h, x:x+w]
roi2 = res[y1:y1+h1, x1:x1+w1]
print(plan1, plan2)
cv2.rectangle(res, (x-5,y-5), (x+w+5, y+h+5), (255,255,0), 2)
cv2.rectangle(res, (x1-5,y1-5), (x1+w1+5, y1+h1+20), (255,255,0), 2)'''
I tried to remove the black color from the image, if i tried the text also removed.
import cv2
img = cv2.imread('test.png')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
thresh1 = cv2.threshold(s, 100, 255, cv2.THRESH_BINARY)[1]
thresh2 = cv2.threshold(v, 150, 255, cv2.THRESH_BINARY)[1]
thresh2 = 255 - thresh2
mask = cv2.add(thresh1, thresh2)
result = img.copy()
result[mask == 128] = (255, 255, 255)
cv2.imwrite('clear.png', result)
Please give some ideas to get the text from that image.
Hello everybody before you close this question, i have already searched here, here too and also here
I am using python code to detect the Leaf in an image, using contours finding and then find out the largest contour, the part works best, but then i want only the leaf part of the image and skip the rest of the image to avoid unnecessary content in the resultant output, some of the methods in the link suggests bounding box but this still includes extra content in the image as the shape is not rectangular it's irregular, sample is attached
The code is following
import cv2
import numpy as np
img = cv2.imread("blob.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.zeros_like(img)
cv2.drawContours(mask, big_contour, -1, 255, -1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
cv2.imwrite('output.jpg', out)
Now the problem is i am getting the resultant image as black nothing cropped all black pixels
There is a problem with your contour because it is not circulating the leaf as the end of the leaf on the right is out of the image.
You can see this when I try to fill the contour to create a mask using
cv2.fillPoly(mask, pts =[big_contour], color=(255,255,255))
it doesn't fill the leaf.
However I tried something although not perfect and has some background left but it crops the leaf to some extend.
import cv2
import numpy as np
img = cv2.imread("96Klg.jpg", -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
#blob = 255 - cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
cnts = cv2.findContours(blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(cnts) == 2:
cnts = cnts[0]
else:
cnts = cnts[1]
big_contour = max(cnts, key = cv2.contourArea)
blob_area_thresh = 1000
blob_area = cv2.contourArea(big_contour)
if blob_area < blob_area_thresh:
print("Leaf is Too Small")
else:
#problem starts from here . i tested big_contour is just perfect by drawing on actual image
mask = np.ones_like(img)
mask.fill(255)
cv2.fillPoly(mask, pts =[big_contour], color=(0,0,0))
#cv2.drawContours(mask, big_contour, -1, (255,255,255), 1)
out = np.zeros_like(img)
out[mask == 255] = img[mask == 255]
width = int(gray.shape[1] * 0.25)
height = int(gray.shape[0] * 0.25)
dim = (width, height)
# resize image
resized = cv2.resize(out, dim, interpolation = cv2.INTER_AREA)
resizedmask = cv2.resize(mask, dim, interpolation = cv2.INTER_AREA)
cv2.imshow('gray',resized)
cv2.imshow('out',resizedmask)
Output
I try to preprocess my train data with resizing, grayscale, and threshold but got error like the title, here is the code
train_image = []
for i in tqdm(range(train.shape[0])):
img = image.load_img('Data/Images/'+ str(train['Id'][i]) + '.jpg', target_size=(400,400,1))
img = cv2.imread('Data/Images/', cv2.IMREAD_GRAYSCALE)
retval,img = cv2.threshold(img, 128, 1, cv2.THRESH_BINARY)
img = image.img_to_array(img)
img = img/255
train_image.append(img)
X = np.array(train_image)
how to write the right code to preprocess the data?
in line
img = cv2.imread('Data/Images/', cv2.IMREAD_GRAYSCALE)
no image is specified. It is just a directory.
Use only one method to load images either image.load_image() or cv2.imread()
I corrected the code by this
train_image = []
for i in tqdm(range(train.shape[0])):
img = cv2.imread('Data/Images/' + str(train['Id'][i]) + '.jpg' ,cv2.IMREAD_GRAYSCALE)
retval,img = cv2.threshold(img, 128, 1, cv2.THRESH_BINARY)
img = cv2.resize(img, (400,400))
img = image.img_to_array(img)
img = img/255
train_image.append(img)
X = np.array(train_image)
I am trying to set dpi to (72,72) using opencv in python. can anyone tell me how do we assign dpi in opencv using imwrite ?
img = cv2.imread('kitchen.jpeg')
img = cv2.resize(img,(500,500),interpolation=cv2.INTER_AREA)
img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (10,10,500,500)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]
lower_white = np.array([0, 0, 0], dtype=np.uint8)
upper_white = np.array([0,0,0], dtype=np.uint8)
mask = cv2.inRange(img, lower_white, upper_white) # could also use threshold
res = cv2.bitwise_not(img, img, mask)
cv2.imwrite('kitchen_processed.jpeg',img)
emm dpi is for printing only. during image write you dont have to do it or you cant do it(for some format). It's only at image print you have to do it. if you insist of doing so. After image write, call System.call with the file name as the variable
convert -density 75 -units pixelsperinch infile.jpg outfile.png
normally the density is 300 or more. in your case, use it at 75
edit
Add this in. Not sure if you have other potential problem. But based on your title, add this line should solve your issue
import subprocess
img = cv2.imread('kitchen.jpeg')
img = cv2.resize(img,(500,500),interpolation=cv2.INTER_AREA)
img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (10,10,500,500)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]
lower_white = np.array([0, 0, 0], dtype=np.uint8)
upper_white = np.array([0,0,0], dtype=np.uint8)
mask = cv2.inRange(img, lower_white, upper_white) # could also use threshold
res = cv2.bitwise_not(img, img, mask)
cv2.imwrite('kitchen_processed.png',img)
subprocess.run('convert -density 75 -units pixelsperinch kitchen_processed.png outfile.png')